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Cracking the Code to Better Quality and
Financial Outcomes
Session 37, February 12, 2019 (1:30-2:30)
James Grana, Ph.D., Chief Analytics Officer, Rush Health
Bala Hota, MD, Vice President & Chief Analytics Officer, Rush University Medical Center
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James R. Grana, Ph.D.
Bala Hota, MD
Has no real or apparent conflicts of interest to report.
Conflict of Interest
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• Review Market Justification for Code Capture Optimization
• Review Relationship Between Code Capture and Financial
Performance
• Review Practical Challenges to Implementing a Code Capture
Strategy
• Review Relationship Between Code Capture and Select Quality
Indicators
Agenda
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• Learning Objective 1: Identify the correlation between accurate
documentation and improved quality scores and financial
outcomes
• Learning Objective 2: Analyze the steps the Rush Health and
RUMC team used to drive process improvements at the provider,
practice, and departmental levels
• Learning Objective 3: Demonstrate the improvements in quality
scores and risk adjustment Rush Health and RUMC achieved by
increasing documentation accuracy
Learning Objectives
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Evolving Health System
• Fee For Service
• Fee For Value
– Shared Savings
– Shared Risk
– Bundles
– MSSP Changes
– Medicare Advantage
– Oncology Care Model
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Risk Adjusted Performance
Measurement and Compensation
• Medicare
• Medicaid
• Commercials
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Benefits of Code Capture Optimization and
Consistency
• Reimbursement
• Quality
– Follow-Up Trigger
– Follow-Up Indicator
• Identify Care Variation Rather than Coding Variation
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Simple Case is Observed to Expected
Ratio
• Simple to Understand
– O/E = 1 suggests that you are performing as expected.
– O/E = 1.1 suggests that you are performing 10% higher than
expected
– O/E = .90 suggests that you are performing 10% lower than
expected
• Considerations
– Comparison Cohort
– Factors Included In Adjustment
– Data Staging and Timing
– Sample Size
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Risk Adjustment Factors and Cost Targets
$0
$200
$400
$600
$800
$1,000
$1,200
$1,400
Moderate RAF: Target $1,000
PMPM
Performance Relative to Target
Assuming a PMPM of $1,000
RAF- Driven Target
PMPM: $1,000
RAF- Driven Target
PMPM: $1,000
Achieved PMPM:
$1,000
Achieved PMPM:
$1,000
When the RAF-driven
Target PMPM and
Achieved PMPM are
equal, dollars are
neither available for
shared savings nor at
risk.
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$0
$200
$400
$600
$800
$1,000
$1,200
$1,400
Moderate RAF: Target $1,000
PMPM
Performance Relative to Target
Assuming a PMPM of $1,000
RAF-Driven Target
PMPM: $750
RAF-Driven Target
PMPM: $750
Achieved PMPM:
$1,000
Achieved PMPM:
$1,000
Dollars at Risk
When the RAF-driven
Target PMPM is below the
Achieved PMPM, dollars
are at risk.
Risk Adjustment Factors and Cost Targets
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$0
$200
$400
$600
$800
$1,000
$1,200
$1,400
Moderate RAF: Target $1,000
PMPM
Performance Relative to Target
Assuming a PMPM of $1,000
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Achieved PMPM:
$1,000
Achieved PMPM:
$1,000
Shared Savings
When the RAF-driven
Target PMPM is above
the Achieved PMPM,
dollars are available for
shared savings.
RAF-Driven Target
PMPM: $1,250
RAF-Driven Target
PMPM: $1,250
Risk Adjustment Factors and Cost Targets
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$0
$200
$400
$600
$800
$1,000
$1,200
$1,400
Low RAF: Target $750 Moderate RAF: Target $1,000 High RAF: Target $1,250
PMPM
Performance Relative to Target
Assuming a PMPM of $1,000
Dollars at
Risk
Shared
Savings
Increasing your RAF, increases your
likelihood of benefiting from shared savings
Risk Adjustment Factors and Cost Targets
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Reduce health care spend
• Curb avoidable utilization
• Utilize lower-cost care
alternatives
Boost premium revenues
• Increase RAF through accurate
HCC capture
• Grow (high-risk) covered lives
Medical Spend
Premium Revenue and
Targets
Two Ways to Improved Your Shared Risk
Performance
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• Hierarchical Condition Categories (HCC) are groupings of ICD-10 diagnosis
codes. Each HCC is assigned a weight.
• RAF scores are the sum HCC weights and demographic weights to reflect the
burden of illness associated with group of patients. CMS uses these scores in its
compensation systems.
Sicker Patients
-> Higher RAF
-> More dollars
Healthier Patients
-> Lower RAF
-> Fewer dollars
What They Are
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• Providers assign diagnoses for a patient’s conditions
• Conditions are sorted into HCCs and assigned a corresponding
numeric weight
• HCCs are re-determined each calendar year, requiring revalidation of all relevant
diagnoses through claims to CMS
Hierarchal Condition Categories
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• Serve as a payment multiplication factor for relevant populations
• Patient HCC weights are summed to create a RAF score
• Scores are recalculated annually
Base Payment Final RAF Final Payment
$1,000 1.0 $1,000
$1,000 1.2 $1,200
$1,000 0.8 $800
Risk Adjustment Factors
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All Codes Missing Diabetes
Age/Gender: 72/M 0.379 0.379
Vascular disease with complications (HCC 107) 0.4 0.4
Morbid obesity (HCC 22) 0.273 0.273
Congestive heart failure (HCC 85) 0.323 0.323
- CHF/diabetes interaction (HCC 85d) 0.154 Not coded
Diabetes with chronic complications (HCC 18) 0.318 Not coded
RAF Sum 1.847 1.375
PMPM $2,124.05 $1,581.25
PMPY $25,488.60 $18,975.00
The Importance of ICD 10 Code
Accuracy
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• Missed diagnoses
• Failing to document/bill for an unresolved condition present in a previous calendar year
• Using a “history of” diagnosis when condition is actively occurring and not yet resolved
• Using a generalized or non-specific diagnosis code
• Failing to document completion of medication list review
Common Coding Errors
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• Patient JW
• Provider: Dr. X
• Conditions not re-validated
• Congestive Heart Failure 0.368
• Chronic Obstructive Pulmonary Disease 0.346
• Coagulation Defects and Other Hem. Disorders 0.252
*Denotes the revenue from CMS; actual payments to provider may vary by payer arrangement
HCC Score Revenue
2016 1.261 $1,450
2017 (October YTD) 0.295 $339
-$1,111*
MA HCC Example
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194 (29.8%) of 651 patients experienced a decrease in HCC score from 2016 to 2017 (October YTD)
– Average decrease of 0.56
– Lost plan revenue* approximately $1,499,232
– If provider were to reach 2,000 members lost revenue would amount to approximately $4,605,888
Common conditions not revalidated
*Denotes the revenue received by insurance carrier from CMS for the population.
Missing Condition Patient Count
Vascular Disease
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Congestive Heart Failure
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Chronic Obstructive Pulmonary Disease
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Coagulation Defects and Other Specified Hematological Disorders
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Breast Prostate and Other Cancers and Tumors
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Acute Renal Failure
18
Specified Heart Arrhythmias
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Ischemic or Unspecified Stroke
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Morbid Obesity
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Diabetes with Chronic Complications
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Sample Reporting: MA HCC YTD
Performance
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Impediments to Code Capture
Improvement Strategies
• Historical Inpatient Focus is Not Enough
• Outpatient Opportunities
• Multiple Risk Adjustment Models (some requiring recoding prior to
each new “episode” or “trigger admission.”)
• Coding Value Chain and Possible Leakage
– Provider
– Input Mechanism
– Internal Coding Team
– Provider IT and ETL
– Payer IT and ETL
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• Legal Considerations
• Disproportionate PCP Burden
• Specialist Reluctance
• Workflow Inefficiencies
• Need for Ongoing Provider Education
Additional Code Capture Considerations
and Impediments
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Comorbidity Capture and
Inpatient Quality
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• Complex space of multiple rating agencies
• Common factors but areas of difference
• Common themes with outpatient ACO risk adjustment
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Quality Measurement & Inpatient Care
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Vizient CMS US News Leapfrog
Consumer
Reports
Truven
Mortality
26% 22% 38% 20% 20%
Efficiency / Cost
6% 8%* 20% 30%
Safety
26% 22% 5% 50% 20% 20%
Effectiveness /
Readmission and
Throughput
21% 26%** 20% 20%
Patient Centeredness
16% 22% 15% 20% 10%
Equity
5%
Reputation
28%
Structural Measures
30% 35%
*4%
- effectiveness of care/ 4% Efficient use of medical
imaging
**22%
- readmission/4% - Timeliness
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Do These Systems Complement Each
Other or Conflict?
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• Overall composite score for hospitals based on Hospital Compare Data
• 64 potential quality measures in 7 domains
• Not all measures reported by all hospitals
• In general:
– Reporting fewer measures was better: fewer than 10% of hospitals
reporting fewer than 38 measures included received either 1 or 2 stars
– AAMCs disproportionately received 1 or 2 stars (62%) (worst)
– Rush: 4 stars; best AAMC in Chicago area, top 15.8% of teaching
hospitals nationally
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CMS Stars Rating
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• Mortality (MI, CABG, COPD, CHF, PNA, CVA)*
• Safety of Care (CLABSI, CAUTI, SSI, MRSA BSIs, C diff, surgical
complications and PSIs)
• Readmission (unplanned readmissions)*
• Patient Experience (Patient Sat Surveys)
• Effectiveness of Care (Vaccination, Screening, Protocol Driven Care)
• Timeliness of Care (ED throughput, time to care for MI)
• Efficient Use of Imaging (Outpatient MRI, CT, and Stress Test Use)
• Dates of Data: July 1, 2013 – June 30, 2016
*Risk Adjusted Measure, with comorbidities affecting expected rate
Star Rating CMS Measure Components
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Based on measured values and case mix adjustment rates
Denominator
Claims Based
(ICD/CPT
Code)
Risk
Adjustment
Expected Rte
Claims Based
Numerator
Claims, Chart
Abstraction
Based
O:E Ratio
Quality Rank Contingent
on Documentation and
Hospital Billing Codes
(HCCs)
CMS Mortality, Safety, & Readmission
Rates
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• By-products of risk adjustment methodology
– Comorbidity adjustment
– Hospital size adjustment (small hospitals get handicap)
– No adjustment for SES
Factors in risk adjustment
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Clinical documentation impacts reimbursement and quality measurement for
hospitals
• Medicare Reimbursements: Maximizing revenue requires determining the
correct Medicare DRG
• Quality: Accurately reflecting outcomes requires fully recording patient and
treatment data, including any complications or comorbidities (CC) and/or major
complications or comorbidities (MCC). Quality risk adjustment models align with
HCCs.
Documentation is often incomplete due to:
- Omission of chronic conditions
- Diagnostic laboratory tests are not fully recorded
Incomplete documentation affects Medicare reimbursement and accurate quality
measurement
Problem: Incomplete Documentation
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20% of a hospital’s revenues are fixed rate Medicare payments
Mechanism Description
Impact of Incomplete
Documentation
Centers for Medicare and Medicaid
(CMS) Reimbursements
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Reimbursements based on illness severity
Lost Revenue
Hospital Value Based
Purchasing (HVBP)
&
Hospital Readmission
Reduction Program (HRRP)
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Patients are assigned a benchmark
probability of readmission/mortality
-
Hospitals are incentivized based on
readmission/mortality vs. benchmark
Financial Penalties
Incomplete documentation affects Medicare reimbursement
through three primary mechanisms:
Financial Impact
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Inaccurate quality measurement affects national
reputation
• Quality metrics such as Patient Safety Indicators
(PSI) influence national rankings and
benchmarking
• Correctly reporting case mix and mortality
measures is critical for accurate quality metrics
• Reputation affects the ability to attract patients
and top staff
Reputational Impact
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Health and Human Services’
“Triple Aim” Goal
1. Improving health care quality
2. Improving population health
3. Reducing unnecessary health care
costs
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Quality Metrics and Outcomes will Drive Medicare
Reimbursements in the Future
Also have reputational impact –public information
Increasing Focus on Quality
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Chronic Conditions
Cancer
Ischemic heart
disease
Major depression
Osteoporosis
Epilepsy
COPD
Hypercholesterolemia
Osteoarthritis
Obesity
Dementia
Malnutrition
Cerebrovascular disease
Hypertension
Asthma
Chronic kidney disease
Bipolar disorder
Congestive heart
failure
Diabetes melitus
• Chronic conditions are critical common factors between DRG and
HCC coding
• Additional codes determine DRG code reclassification and optimize
Hierarchical Condition Classification (HCC) scoring
• Results in a greater reimbursement and improved quality scores
Conditions Detected
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Percentile
1 - 9 points
0 points
10 points
Percentile of evaluated hospitals
Rush’s Risk Adjusted Survival Rate
Included in Medicare Data
Missing from Medicare Data
*Only the conditions with missing
codes are shown (8 of 25)
Hypothetical
1%
improvement:
94
th
percentile
Original:
77
th
percentile
Impact of Conditions on AMI 30-Day
Mortality
Rush’s Actual Survival Rate
Rush’s Expected Survival Rate, with the missing condition
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Results of Implementation
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Total Hip/Total Knee Readmission
Penalty
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Implementation of software resulted in
pay-for-performance improvements at Rush
Federal Fiscal Year
Value Based
Purchasing
Hospital Readmission
Reduction Program
Hospital Acquired
Conditions Reduction
Program
Net Pay for
Performance
FY2015 $550 K ($1.2 M) ($1.7 M) ($2.4 M)
FY2016 $676 K ($1.1 M) No Penalty ($676 K)
FY2017 $958 K ($483 K) No Penalty $475 K
Pay for Performance Initiatives
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CMS Pay for Performance Estimated Returns for Federal FY2017 CMS Star Rankings
VBP
Readmission
Reduction
HAC Total
Patient Experience
Stars
Overall Ranking
RUMC $958K ($483K) – $475K
U of C $19K ($341K) – ($322K)
NM $562K ($386K) ($1,989K) ($1,813K)
UIC ($129K) ($136K) – ($265K)
Loyola ($35K) ($320K) ($1,611K) ($1,967K)
Post-Implementation: Rush’s outperforms its peers
Rush’s Comparison to Peers
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James Grana, Ph.D., Chief Analytics Officer, Rush Health
Bala Hota, MD, Vice President & Chief Analytics Officer,
Rush University Medical Center
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James Grana, Ph.D.
Chief Analytics Officer
Rush Health
James_Grana@rush.edu
Bala Hota, MD
Vice President & Chief Analytics Officer
Rush University Medical Center
Bala_Hota@rush.edu
Contact Information
